Comparison of heavy episodic drinking patterns between Korean and Chinese immigrants
Bibliographic record
Abstract
Ruderman, D., Clapp, J., Hofstetter, C., Irvin, V., Kang, S., & Hovell, M. (2016). Comparison of heavy episodic drinking patterns between Korean and Chinese immigrants. The International Journal Of Alcohol And Drug Research, 5(2), 65-71. doi:http://dx.doi.org/10.7895/ijadr.v5i2.220Objective: Drinking-related problems are increasing among Asian immigrants despite the popular perceptions of a “model minority.” Sociocultural factors may relate to differing drinking patterns among subsets of Asian American populations. This study explores the relationship between nationality and alcohol consumption among Chinese and Korean Americans, specifically in regards to acculturation and social networks.Method: First-generation Chinese and Korean immigrants residing in the greater Los Angeles area were recruited (N= 2715). Structured interviews were conducted over the phone and by professional bilingual interviewers in the language of participant preference.Results: Although subsamples were demographically similar, Chinese immigrants were less likely to report heavy episodic drinking (HED) than Korean immigrants. Participants in each group with social networks composed of drinkers or problem drinkers and those that encouraged drinking were more likely to report HED themselves.Conclusions: Alcohol consumption and its dynamics are impacted by peer networks among first-generation Chinese and Koreans residing in the United States. While drinking behaviors differ for Chinese and Korean immigrants, the impact of peer’s drinking behaviors on one’s own drinking is paramount. This result has important implications for interventions and the need for further research focusing on the impact of peer interactions and alcohol use among this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".